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Why is Redis Fast? Redis is fast for in-memory data storage. Its speed has made it popular for caching, session storage, and real-time analytics. But what gives Redis its blazing speed? Let's explore: RAM-Based Storage At its core, Redis primarily uses main memory for storing data. Accessing data from...

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Sahn Lam

48,505 subscribers

46,910 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 8

Фото профиля LS
LS2 лет назад

Threads same as the CPU cores. I believe vertex does something similar.

Фото профиля Abhishek Yadav
Abhishek Yadav2 лет назад

Insight full after reading, Thanks for sharing ...

Фото профиля Jake
Jake2 лет назад

> how should we configure it to fully utilize all the CPU cores of modern hardware when deploying in production? You can't. You're going to be bottlenecked on that single thread doing all the data access. Best you can do is run multiple redis instances or use something else.

Фото профиля Vinay
Vinay2 лет назад

Thanks for sharing

Фото профиля Suresh Jayanthi
Suresh Jayanthi2 лет назад

With the #ibmlinuxone one can have up to 40 TB of memory and 4 levels of cache and the #s390x cpu architecture excels at single threaded performance. You may want to explore this angle

Фото профиля Who Am I
Who Am I2 лет назад

+

Фото профиля Gomaji Kapse
Gomaji Kapse2 лет назад

Can use multi threading at I/O level by splitting the socket fds per thread, which gets run on their own core.

Фото профиля Alex 📚 PromptLeo
Alex 📚 PromptLeo2 лет назад

It is indeed fascinating that Redis is single threaded and that’s still enough for the performance in comcurrent environments. Is this because network bandwidth is lower than disk access anyway?

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Redis in 60 Second 👇 What is Redis ? Redis = REmote DIctionary Server An in-memory data store used mainly as: - Cache - Session store - Message broker - Real-time data store It’s insanely fast because data lives in RAM, not on disk. 🟢 Why Redis exists Typical flow without Redis: User → Application → Database - Database is slow - High traffic = high DB load - Latency increases - DB becomes a bottleneck - This doesn’t scale. 🟢 Redis comes into the picture - Redis sits between Application and Database New flow: User → Application → Redis → Database - Redis acts as a fast access layer 🟢 Complete Redis request flow 1️⃣ User sends a request 2️⃣ Application checks Redis first 3️⃣ If data exists in Redis → Cache HIT Data returned in milliseconds 4️⃣ If data not found → Cache MISS App fetches data from Database Stores it in Redis with TTL - Returns response to user - This is called Cache Aside Pattern 🟢 Where Redis is used in real systems? - Login sessions - API response caching - Rate limiting - Leaderboards - Real-time analytics - Background jobs / queues - Almost every high-traffic app uses Redis. 🟢 Redis in DevOps & System Design : As a DevOps, Redis knowledge is used in: - Application performance tuning - Scaling backend systems - Reducing DB cost - Designing high-availability systems Thanks for reading . Happy Learning !
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Redis in 60 Second 👇 What is Redis ? Redis = REmote DIctionary Server An in-memory data store used mainly as: - Cache - Session store - Message broker - Real-time data store It’s insanely fast because data lives in RAM, not on disk. 🟢 Why Redis exists Typical flow without Redis: User → Application → Database - Database is slow - High traffic = high DB load - Latency increases - DB becomes a bottleneck - This doesn’t scale. 🟢 Redis comes into the picture - Redis sits between Application and Database New flow: User → Application → Redis → Database - Redis acts as a fast access layer 🟢 Complete Redis request flow 1️⃣ User sends a request 2️⃣ Application checks Redis first 3️⃣ If data exists in Redis → Cache HIT Data returned in milliseconds 4️⃣ If data not found → Cache MISS App fetches data from Database Stores it in Redis with TTL - Returns response to user - This is called Cache Aside Pattern 🟢 Where Redis is used in real systems? - Login sessions - API response caching - Rate limiting - Leaderboards - Real-time analytics - Background jobs / queues - Almost every high-traffic app uses Redis. 🟢 Redis in DevOps & System Design : As a DevOps, Redis knowledge is used in: - Application performance tuning - Scaling backend systems - Reducing DB cost - Designing high-availability systems Thanks for reading . Happy Learning !

Nandkishor

22,337 просмотров • 8 месяцев назад

i built a very fast, virtualized filesystem for ai agents built with Redis, Vercel AI SDK and just-bash agents can search and grep files really fast because it's all in redis memory
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i built a very fast, virtualized filesystem for ai agents built with Redis, Vercel AI SDK and just-bash agents can search and grep files really fast because it's all in redis memory

Josh tried coding

73,390 просмотров • 7 месяцев назад

Recently, Shopify switched an integral part of their shopping cart system from Redis to MySQL. Is Redis dead?
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Recently, Shopify switched an integral part of their shopping cart system from Redis to MySQL. Is Redis dead?

PlanetScale

17,685 просмотров • 1 месяц назад

Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.
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Start building for an agent-first world. If you have a product, you need to start offering skills for Claude, Codex, Cursor, and any other agents. Your skills should specify: • How to navigate and use your product • Best practices the agent must follow • Detailed instructions on how to accomplish things • Anti-patterns to avoid Redis is one of the most popular in-memory data stores in the world, and they just released their agent skills. It takes one second to install, and it will turn your agent into a Senior Redis Engineer: $ npx skills add redis/agent-skills In the attached video, I show you how to install it as a plugin in Claude Code and some of its benefits. This is the easiest way to "teach" models what they don't know and keep their knowledge up to date. If you ask me, skills is literally one of the most brilliant ideas that Anthropic has put out there. If you use Redis, their skill is a must-have. If you don't, this skill will show you how to build and structure yours. Here is what their skill teaches your agent: 1. Current patterns for common use cases: caching, rate limiting, session management, vector search, semantic caching, pub/sub, streams. 2. Which data structure to use and when: hashes vs. JSON vs. sorted sets vs. vector sets. 3. Anti-patterns to avoid: no KEYS in loops, no unbounded key growth, no large values that amplify every operation. 4. Production-aware defaults: connection pooling, pipelining, cluster compatibility, error handling that doesn't silently swallow failures.

Santiago

37,546 просмотров • 8 месяцев назад

I had my interview at a Media startup from Seoul as a Backend Engineer This was asked from me to build during the interview call and not to use any AI code editor .. I had to rely on docs and other people's code from stack overflow Its a replica of multi gigabyte video upload component from their core infra which I had to build and explain ... > has a grpc api for chunked exports > using redis to track the chunk assembly state like which chunks are received and expiration of chinks > and a simple html client to show the pause and resume of chunks for uploading Built with go, grpc, postgresql and redis
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I had my interview at a Media startup from Seoul as a Backend Engineer This was asked from me to build during the interview call and not to use any AI code editor .. I had to rely on docs and other people's code from stack overflow Its a replica of multi gigabyte video upload component from their core infra which I had to build and explain ... > has a grpc api for chunked exports > using redis to track the chunk assembly state like which chunks are received and expiration of chinks > and a simple html client to show the pause and resume of chunks for uploading Built with go, grpc, postgresql and redis

Mrinal

61,463 просмотров • 5 месяцев назад

Redis built a cache that cuts LLM costs by 90%! Production LLM apps do not receive completely new questions every time. A customer-support assistant might receive all three of these: - "Can I get a refund after buying the monthly plan?" - "Is the monthly subscription refundable?" - "Can I cancel the plan and get my money back?" The wording is different, but the underlying question and its answer remain the same. Yet LLM apps process every version as a new request. They assemble the prompt, send it to the model, and generate an answer that may have already been generated. Prefix caching reduces part of these repeated calls. When requests begin with the same system prompt or context, the model can reuse the KV states already computed for that shared prefix. But the request still hits the LLM. The new tokens must be processed, and the complete answer must still be decoded. So even with a prefix-cache hit, there's another generation call involved. To solve this, instead of only caching computation inside the model, the application can cache the generated response outside it. When another question arrives, the system embeds it and compares it with previously answered questions. If it finds a sufficiently close match, it returns the stored response without invoking the LLM again. A cache hit removes the input tokens, output tokens, and decoding time associated with another LLM call. In practice, it is important to decide which questions can safely share an answer since a production setup needs well-tuned similarity thresholds, expiration policies, data isolation, and monitoring for incorrect matches. If you want to use this in practice, Redis already implements it as a managed service called Redis LangCache. Under the hood, it generates embeddings, searches previous responses, and returns a matching answer before another model call occurs. Redis also handles access scopes, custom filtering, TTL and eviction controls, and cache monitoring through Redis Cloud. I built an interface to compare it against direct LLM inference. The video below shows this in action, and I worked with Redis on this post to put this together. For the paraphrased question in my run, direct inference took 2.232 seconds and consumed 514 input tokens plus 250 output tokens. Redis returned the earlier response in 0.37 seconds with zero LLM input or output tokens. That was roughly 6x faster in this run. Redis reports API cost savings of up to 90% and cache-hit responses up to 15x faster. The actual result depends on how much safe repetition exists in the workload. You can try Redis LangCache here: If you want to dive deeper, I have already written a detailed breakdown of KV, prefix, prompt, and semantic caching in the article quoted below. This demo builds on the final technique and shows it running in practice. Read it below.
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Redis built a cache that cuts LLM costs by 90%! Production LLM apps do not receive completely new questions every time. A customer-support assistant might receive all three of these: - "Can I get a refund after buying the monthly plan?" - "Is the monthly subscription refundable?" - "Can I cancel the plan and get my money back?" The wording is different, but the underlying question and its answer remain the same. Yet LLM apps process every version as a new request. They assemble the prompt, send it to the model, and generate an answer that may have already been generated. Prefix caching reduces part of these repeated calls. When requests begin with the same system prompt or context, the model can reuse the KV states already computed for that shared prefix. But the request still hits the LLM. The new tokens must be processed, and the complete answer must still be decoded. So even with a prefix-cache hit, there's another generation call involved. To solve this, instead of only caching computation inside the model, the application can cache the generated response outside it. When another question arrives, the system embeds it and compares it with previously answered questions. If it finds a sufficiently close match, it returns the stored response without invoking the LLM again. A cache hit removes the input tokens, output tokens, and decoding time associated with another LLM call. In practice, it is important to decide which questions can safely share an answer since a production setup needs well-tuned similarity thresholds, expiration policies, data isolation, and monitoring for incorrect matches. If you want to use this in practice, Redis already implements it as a managed service called Redis LangCache. Under the hood, it generates embeddings, searches previous responses, and returns a matching answer before another model call occurs. Redis also handles access scopes, custom filtering, TTL and eviction controls, and cache monitoring through Redis Cloud. I built an interface to compare it against direct LLM inference. The video below shows this in action, and I worked with Redis on this post to put this together. For the paraphrased question in my run, direct inference took 2.232 seconds and consumed 514 input tokens plus 250 output tokens. Redis returned the earlier response in 0.37 seconds with zero LLM input or output tokens. That was roughly 6x faster in this run. Redis reports API cost savings of up to 90% and cache-hit responses up to 15x faster. The actual result depends on how much safe repetition exists in the workload. You can try Redis LangCache here: If you want to dive deeper, I have already written a detailed breakdown of KV, prefix, prompt, and semantic caching in the article quoted below. This demo builds on the final technique and shows it running in practice. Read it below.

Avi Chawla

166,230 просмотров • 1 месяц назад

Redis built a cache that cuts LLM costs by 70%! Production LLM apps often receive different versions of the same question. For instance, an internal developer assistant might receive: - "How do I rotate an API key?" - "Where can I replace my API key?" The wording is different, but both questions have the same answer. Prefix caching cannot handle this repeated generation because it only reuses computation when the cache matches bit-by-bit. And if the cache hits, an LLM response is still generated again. A semantic cache stores the complete question-response pair outside the model. When a query arrives, it searches for previously answered questions with similar meaning. A valid match returns the stored response with no LLM call. If you want to use this in practice, Redis already implements it as a managed service called Redis LangCache. Under the hood, LangCache embeds the incoming question, searches stored responses, and applies the configured similarity threshold and filters. A cache hit returns the earlier response. A miss falls back to the LLM, after which the new response can be stored for future requests. Redis also handles access scopes, custom filters, embedding selection, TTL, eviction, and monitoring through a REST API, without another database to deploy or manage. While the actual savings depend on how much safe repetition exists in the workload, cache-hit responses are up to 15x faster and 70% cheaper. Try Redis LangCache: I built a small interface comparing LangCache with direct LLM inference. The video below shows this in action, and I worked with Redis on this post to put it together. To dive deeper, my co-founder published a detailed article on KV, prefix, prompt, and semantic caching. Read it below.
0:23

Redis built a cache that cuts LLM costs by 70%! Production LLM apps often receive different versions of the same question. For instance, an internal developer assistant might receive: - "How do I rotate an API key?" - "Where can I replace my API key?" The wording is different, but both questions have the same answer. Prefix caching cannot handle this repeated generation because it only reuses computation when the cache matches bit-by-bit. And if the cache hits, an LLM response is still generated again. A semantic cache stores the complete question-response pair outside the model. When a query arrives, it searches for previously answered questions with similar meaning. A valid match returns the stored response with no LLM call. If you want to use this in practice, Redis already implements it as a managed service called Redis LangCache. Under the hood, LangCache embeds the incoming question, searches stored responses, and applies the configured similarity threshold and filters. A cache hit returns the earlier response. A miss falls back to the LLM, after which the new response can be stored for future requests. Redis also handles access scopes, custom filters, embedding selection, TTL, eviction, and monitoring through a REST API, without another database to deploy or manage. While the actual savings depend on how much safe repetition exists in the workload, cache-hit responses are up to 15x faster and 70% cheaper. Try Redis LangCache: I built a small interface comparing LangCache with direct LLM inference. The video below shows this in action, and I worked with Redis on this post to put it together. To dive deeper, my co-founder published a detailed article on KV, prefix, prompt, and semantic caching. Read it below.

Akshay 🚀

88,731 просмотров • 1 месяц назад

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!
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New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!

Andrew Ng

62,497 просмотров • 11 месяцев назад

ProsperoMenu is now out. Repo in replies It is a community expandable overlay system, and elfdir splash screen enabler. (or boot animation with auto-redis from Jordy )
0:25

ProsperoMenu is now out. Repo in replies It is a community expandable overlay system, and elfdir splash screen enabler. (or boot animation with auto-redis from Jordy )

StonedModder

14,040 просмотров • 18 дней назад

Big news: Upstash Redis Search is now generally available! 🎉 ◆ Superfast™ full-text search built in Rust ◆ Clean & 100% type-safe TypeScript APIs ◆ Works with any Upstash Redis database Add fast search to React, Next.js and more 👇
1:00

Big news: Upstash Redis Search is now generally available! 🎉 ◆ Superfast™ full-text search built in Rust ◆ Clean & 100% type-safe TypeScript APIs ◆ Works with any Upstash Redis database Add fast search to React, Next.js and more 👇

Upstash

36,357 просмотров • 4 месяцев назад

This is my assignment for a SF startup as a backend and infra engineer the main goal was to build a workflow engine that executes directed acyclic graphs of tasks for this assessment the task would arbitrary HTTP endpoints. But I replaced it with my own usage of news aggregator from hackernews, github (top repos for VAE, and libs related to Pytorch) and threads on reddit related to topic ML This I can use in many paid gigs too as a CD deployements like test > build > deploy staging > tests > deploy prod > notify but for now it handles simple tasks and its built on go, redis, grpc with htmx and tailwindcss
0:38

This is my assignment for a SF startup as a backend and infra engineer the main goal was to build a workflow engine that executes directed acyclic graphs of tasks for this assessment the task would arbitrary HTTP endpoints. But I replaced it with my own usage of news aggregator from hackernews, github (top repos for VAE, and libs related to Pytorch) and threads on reddit related to topic ML This I can use in many paid gigs too as a CD deployements like test > build > deploy staging > tests > deploy prod > notify but for now it handles simple tasks and its built on go, redis, grpc with htmx and tailwindcss

Mrinal

145,017 просмотров • 9 месяцев назад

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.
7:57

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Brian Roemmele

87,244 просмотров • 2 лет назад

Redis Walkthrough in 60 seconds 🕐
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Redis Walkthrough in 60 seconds 🕐

Cap-EO 👨🏾‍💻

24,435 просмотров • 4 месяцев назад

Moving AI to production? A big chunk of your token bill is spent answering the same question twice - and agents burn ~4x the tokens of chat. Semantic caching fixes it and Redis LangCache does this as a managed layer - they cite up to 90% lower API costs: #ad
2:47

Moving AI to production? A big chunk of your token bill is spent answering the same question twice - and agents burn ~4x the tokens of chat. Semantic caching fixes it and Redis LangCache does this as a managed layer - they cite up to 90% lower API costs: #ad

Addy Osmani

32,820 просмотров • 2 месяцев назад

We implemented an exploit for RediShell (CVE-2025-49844). While doing so, we discovered that the publicly available PoC incorrectly uses loadstring to trigger the Redis UAF. Kudos to Wiz for the interesting findings!
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We implemented an exploit for RediShell (CVE-2025-49844). While doing so, we discovered that the publicly available PoC incorrectly uses loadstring to trigger the Redis UAF. Kudos to Wiz for the interesting findings!

DARKNAVY

41,577 просмотров • 1 год назад

The Vercel Chat SDK now features stream resumption. This makes AI conversations resilient to network hiccups and reloading or sharing a chat mid-generation. This is especially valuable for long responses (e.g.: Deep Research). No proprietary APIs, no sticky load balancing, just Redis pubsub.
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The Vercel Chat SDK now features stream resumption. This makes AI conversations resilient to network hiccups and reloading or sharing a chat mid-generation. This is especially valuable for long responses (e.g.: Deep Research). No proprietary APIs, no sticky load balancing, just Redis pubsub.

Guillermo Rauch

163,675 просмотров • 1 год назад

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 просмотров • 2 лет назад

You know our name. Meet our new brand. Same Redis, still fast.
0:45

You know our name. Meet our new brand. Same Redis, still fast.

Redis

1,151,365 просмотров • 2 лет назад

Everyone is building RAG applications, but nobody is talking about the data these systems use. You are delusional if you think clients will have their data sitting in a folder waiting for you to process it. Data is everywhere: in Google Drive, Dropbox, S3, Gmail, Slack, you name it. And, of course, no sane developer wants to build connections to every one of these systems. This would be suicide. I'm working with Ragie, and they released Ragie Connect to solve this problem. First, their RAG system is top-notch (they have published how they do on several RAG benchmarks), and with Connect, they made it very simple to integrate client data without having to write any code. (Well, in reality, you still have to write a few lines, but it's minimal.) Instead of developing one-off integrations for Drive, Dropbox, etc, you can use Connect to integrate with all of them and let Ragie handle authentication, authorization, and automatic data synchronization. This is a huge time saver!
3:47

Everyone is building RAG applications, but nobody is talking about the data these systems use. You are delusional if you think clients will have their data sitting in a folder waiting for you to process it. Data is everywhere: in Google Drive, Dropbox, S3, Gmail, Slack, you name it. And, of course, no sane developer wants to build connections to every one of these systems. This would be suicide. I'm working with Ragie, and they released Ragie Connect to solve this problem. First, their RAG system is top-notch (they have published how they do on several RAG benchmarks), and with Connect, they made it very simple to integrate client data without having to write any code. (Well, in reality, you still have to write a few lines, but it's minimal.) Instead of developing one-off integrations for Drive, Dropbox, etc, you can use Connect to integrate with all of them and let Ragie handle authentication, authorization, and automatic data synchronization. This is a huge time saver!

Santiago

73,023 просмотров • 1 год назад